K. Burns, Vincent Legout, A. Barbalace, B. Ravindran
{"title":"PrVM","authors":"K. Burns, Vincent Legout, A. Barbalace, B. Ravindran","doi":"10.1145/3373400.3373402","DOIUrl":null,"url":null,"abstract":"We present PrVM, a framework for scheduling real-time VMs on multicore hardware. It addresses the intersection of the following problems: probabilistic real-time scheduling, VM scheduling, and full virtualization. Though each of these problems have been studied, their intersection - motivated by the need to consolidate multiple real-time software stacks, whose applications can be defined via probabilistic timing properties, onto a single embedded platform - is empty. PrVM uses a probabilistic model and timeliness optimality criterion. PrVM schedules VMs as server-like processes, computes time budgets using probabilistic methods, and aggregates task time budgets into VM time budgets. Experimental evaluations, using simulations and a concrete implementation, confirm the framework's effectiveness for synthetic benchmarks and multimedia applications.","PeriodicalId":37024,"journal":{"name":"ACM SIGBED Review","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGBED Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3373400.3373402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
We present PrVM, a framework for scheduling real-time VMs on multicore hardware. It addresses the intersection of the following problems: probabilistic real-time scheduling, VM scheduling, and full virtualization. Though each of these problems have been studied, their intersection - motivated by the need to consolidate multiple real-time software stacks, whose applications can be defined via probabilistic timing properties, onto a single embedded platform - is empty. PrVM uses a probabilistic model and timeliness optimality criterion. PrVM schedules VMs as server-like processes, computes time budgets using probabilistic methods, and aggregates task time budgets into VM time budgets. Experimental evaluations, using simulations and a concrete implementation, confirm the framework's effectiveness for synthetic benchmarks and multimedia applications.